RQDA stores all text in an SQLite database and the package provides a query command to extract data. This code snippet opens the RQDA file and extracts the texts from the database. The transcribed interviews are converted to a text corpus (the native format for the tm package) and whitespace, punctuation etc is removed. Word clouds are a popular method for exploratory analysis of texts. The wordcloud is created with the text mining and wordcloud packages. For reasons of agreed anonymity, I cannot provide the raw data file of the interviews through GitHub. The data consists of the transcripts of six interviews which I manually coded using RQDA. The purpose of these interviews was to learn about the value proposition for water utilities. Below the video, I share an example from my dissertation which compares qualitative and quantitative methods for analysing text.įor my dissertation about water utility marketing, I interviewed seven people from various organisations. The video below contains a complete course in using this software. RQDA assists with qualitative data analysis using a GUI front-end to analyse collections texts. Huang Ronggui from Hong Kong developed the RQDA package to analyse texts in R. The capabilities of R in numerical analysis are impressive but it can also assist with Qualitative Data Analysis (QDA). To fully embrace all aspects of data science we need to be able to methodically undertake qualitative data analysis. Many data scientists are working on automated text analysis to solve this issue (the topicmodels package is an example of such an attempt). These efforts are impressive but even the smartest text analysis algorithm is not able to derive meaning from text. The often celebrated artificial intelligence of machine learning is impressive but does not come close to human intelligence and ability to understand the world. The most recent version of the code is available on my GitHub repository. In this article, I show how I analysed data from interviews using both quantitative and qualitative methods and demonstrate why qualitative data science is better to understand text than numerical methods. This article introduces some aspects of qualitative data science with an example from my dissertation. My dissertation used a mixed-method approach to review the relationship between employee behaviour and customer perception in water utilities. The dynamics of reality are reduced to statistics, losing the narrative of the people that the research aims to understand.īeing both an engineer and a social scientist, I acknowledge the importance of both numerical and qualitative methods. When analysing people, numbers present an illusion of precision and accuracy. Giving primacy to quantitative research in the social sciences comes at a high price. Numerical analysis reduces the complexity of the social world. There is, however, a price to pay when relying on numbers alone. Scientists and professionals consider numerical methods the gold standard of analysis. Even the analysis of text is reduced to a numerical problem using Markov chains, topic analysis, sentiment analysis and other mathematical tools. Data scientists generally solve problems using numerical solutions. Qualitative data science sounds like a contradiction in terms.
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